- Top of page
- DESIGN AND METHODS
- Literature Cited
- Supporting Information
Flow cytometry is a valuable tool in research and diagnostics including minimal residual disease (MRD) monitoring of hematologic malignancies. However, its gradual advancement toward increasing numbers of fluorescent parameters leads to information rich datasets, which are challenging to analyze by standard gating and do not reflect the multidimensionality of the data.
We have developed a novel method to analyze complex flow cytometry data, based on hierarchical clustering analysis (HCA) but with a new underlying algorithm, using Mahalanobis distance measure. HCA is scalable to analyze complex multiparameter datasets (here demonstrated on up to 12 color flow cytometry and on a 20-parameter synthetic dataset).
We have validated this method by comparison with standard gating approaches when performed independently by expert cytometrists. Acute lymphoblastic leukemia blast populations were analyzed in diagnostic and follow-up datasets (n = 123) from three centers. HCA results correlated very well (Passing–Bablok correlation coefficient = 0.992, slope = 1, intercept = −0.01) with standard gating data obtained by the I-BFM FLOW-MRD study group. To further improve the performance in follow-up samples with low MRD levels and to automate MRD detection, we combined HCA with support vector machine (SVM) learning.
HCA in combination with SVM provides a novel diagnostic tool that not only allows analysis of increasingly complex flow cytometry data but also is less observer-dependent compared with classical gating and has potential for automation. © 2011 International Society for Advancement of Cytometry
In childhood acute lymphoblastic leukemia (ALL), response to therapy as measured by minimal residual disease (MRD) monitoring is an important biomarker for predicting relapse and stratifying treatment (1–5). MRD can be assessed by molecular analysis of B- and T-cell receptor gene rearrangements or by flow cytometric analysis of aberrant immunophenotypes. Flow cytometric MRD monitoring is a fast and sensitive method and has been incorporated in several large childhood ALL clinical trials (1, 6, 7). However, flow cytometry generates increasingly large and information-rich datasets, which provide new challenges for analysis. Modern multilaser flow cytometers are able to simultaneously measure up to 12 or more parameters and acquire such information from millions of single cells (8, 9). Traditional gating of populations on two-parameter plots is tedious (e.g., 28 plots in six-color flow cytometry, 66 plots for 10-color analysis, 91 plots for 12-color analysis, etc.) and does not reflect the multidimensionality of the data. Moreover, both the setting of the gates and interpretation of the results are observer-dependent and require intensive training and high levels of expertise. Therefore, new analytical tools that are less observer-dependent reflect the multidimensionality of the data and enable automatization are needed. This would facilitate the more widespread use and applicability of flow cytometry for MRD monitoring within large international multicenter trials.
Alternative analytical methods have been tested for flow cytometry data (10–16), but most of them rely on prior knowledge of the number of clusters (cell populations) expected in the sample. These methods produce limited number of clusters without information on their inner hierarchy (subpopulations). Although hierarchical clustering methods can place each cell in its hierarchical context within the analyzed dataset, their downside is that they often fail to reflect the elliptical shapes of flow cytometric populations. One suggested solution lies in the use of Mahalanobis distance measurement for computing the distance of clusters (17); however, this method was unable to cluster samples from single events, a feature necessary for the detection of small populations such as in monitoring MRD in patients with leukemia. Therefore, we have developed a novel algorithm, harboring the advantages of hierarchical clustering and using Mahalanobis distance measurement, but which has the ability to cluster data from single events. With this approach, it is possible to present complex flow data in one figure, yet allowing easy separation of subpopulations and their quantification. Here, we validate this novel analysis algorithm in 123 MRD datasets from 45 patients with ALL (including data from 38 patients from the I-BFM FLOW-MRD study group QC investigations, similar to recently published work) (18, 19).
- Top of page
- DESIGN AND METHODS
- Literature Cited
- Supporting Information
Both for hematological research and diagnostics, modern multiparameter flow cytometry is a powerful tool for phenotyping normal and leukemic cell populations (27). One of its key applications lies in the monitoring of MRD as a clinically important biomarker for relapse prediction and treatment stratification (1–5).
However, the rapidly rising complexity of multiparameter flow cytometry datasets creates new challenges. Currently, the analytical standard in the field involves gating, in which one or more gates are defined in each histogram or dual parameter plot and a sequence or combination of gates defines the population of interest. This process is tedious or even unfeasible, already requires highly experienced cytometrists and is observer dependent. Most importantly, the sequential gating is limited in its ability to reflect the multidimensionality of the data. A solution to both the multidimensionality of flow data and the observer dependency lies in the usage of unsupervised learning methods.
A number of methods have been suggested for the use in flow cytometry (10–16). Most methods rely on estimate of number of populations leaving behind hierarchical nature of complex biological sample or use the hierarchy of clusters only as a proxy for building models of estimated number of components/clusters (28). HCA, on the other hand, offers a picture of all recorded events in a hierarchically organized fashion so that cells with similar characteristics reside close to each other. HCA in its standard form measures distances between data points once and then uses this distance in the linkage part of the algorithm for merging clusters and thus building the hierarchy. This setting, however, does not reflect the ellipsoidal shape of populations in flow cytometry datasets and, therefore, there has been a lack of unsupervised learning methods applicable to flow cytometry.
We have developed a new algorithm for HCA, using adaptive Mahalanobis-average linkage, to cluster flow data. In this algorithm, merging of clusters is based on distances of data points of one cluster to an ellipsoid fitted to another cluster and vice versa. Mahalanobis-average linkage allows the formation of clusters starting from single cells. This feature has two important advantages over some previous methodologies (17): the major one being that clustering from single cells increases the sensitivity of population detection, which has important implications for both MRD monitoring and explorative analyses of flow data. Second, Mahalanobis-average linkage also allows clustering in pure HCA manner without the need of initial data splitting thus avoiding any possible introduction of errors in this first step.
As a marker of quality, populations chosen as clusters from HCA using Mahalanobis-average linkage, usually have an even distribution of measured cell parameters. HCA dissects such populations not only when they are relatively well separated from each other but also when they are overlapping with other populations in the sample and therefore would be difficult or even impossible to find using a traditional gating approach. This was exemplified by the distinct CD38+B-lin− population in Figure 1.
Using this novel HCA algorithm, it became possible to correctly assign both the immunophenotype and the percentage of leukemic blasts, in a large cohort of diagnostic and follow-up samples from children with ALL. There was an excellent correlation between leukemic levels determined by traditional gating and HCA. The correlation was equally high for both BCP-ALL and T-ALL samples, even if T-ALL is traditionally more challenging due to interfering normal cell populations, blast heterogeneity or loss of immaturity associated markers. Most importantly, this correlation compared well with the interlaboratory QC investigations recently reported from the I-BFM FLOW-MRD study group (18). This comparison served as validation of our method, demonstrating the potential of this new approach for clinical use.
The biggest challenge for HCA using this algorithm is the size of dataset, which can be analyzed on current desktop computers, which is limited to ∼2 × 104 events. The sensitivity of any flow cytometry analysis is limited by the number of events recorded and by the minimal number of cells recognized as a population. For 104 events, datasets analyzed by HCA in this work using 10 cells as a threshold for defining a population, the sensitivity is 0.1%. This can be overcome in MRD monitoring, where high numbers of cells must be analyzed, by usage of SVM. SVM is a supervised learning method able to automatically detect populations of interest in datasets with high numbers of acquired events (>106, data not shown). The combination of hierarchical clustering and SVM allowed detection of low levels of residual disease population in follow-up ALL samples. Other possibilities, not presented here, to overcome cell number limitations is either use SVM on populations derived from other methods (e.g., binning) or to split the data before HCA is applied (either manual—subgating or the sequence of HCA on representative subset > SVM of chosen subset > HCA on subset defined by SVM).
Other challenges for flow cytometric MRD monitoring and thus for HCA or SVM analysis include immunophenotypic shifts in ALL blasts following therapy, as well as the discrimination of persisting leukemic blasts from regenerating normal hematogones (29–31). In principal, any supervised learning is prone to be affected by significant changes in the parameters of the population of interest, and these issues will need further investigation. In our hands, immunophenotype modulation did not hamper HCA or SVM analysis (Fig. 5). Regenerating populations, detected with HCA, were, however more challenging for SVM (Fig. 6). This difficulty may be addressed by training the classifiers on several classes simultaneously, for example, both by positive training on the class of interest (here: the malignant population) and by negative training on the remaining data points (here: residual normal bone marrow).
Despite these challenges, HCA using Mahalanobis-average linkage opens up a new perspective on how to view flow cytometry data. The inherent multidimensional nature of the analysis leads to identification of homogenous populations not only on histograms or two-parameter dot plots but also in the n-dimensional space (with n representing the number of parameters analyzed). This method can be up-scaled and is easily applicable to modern cutting edge high parameter flow cytometry that is otherwise difficult to analyze. In addition, HCA has the ability to show sample populations not only in the hierarchical context of other populations but also with their inner hierarchy. Dissecting tumor heterogeneity is key to understanding clonal evolution (32), development of drug-resistance (33) or identifying candidate leukemia-propagating stem cell populations (34).
Most important for its clinical applicability, HCA is less observer dependent than traditional gating. By reanalyzing 23 follow-up samples from the I-BFM FLOW-MRD study group (18), we have been able to achieve high concordance with standardized flow cytometry analysis, without participating in the respective intensive training and feedback framework. SVM testing is completely observer independent, once the classifier is trained for the recognition of residual disease population, and can be fully automatic. However, the choice of population to train the classifier still is observer dependent and relies on the method used. As HCA clusters form compact populations that provide optimal classes for SVM it appears to be advantageous to combine HCA with SVM. HCA has the ability of identifying various relevant populations without previous knowledge of the sample, whereas SVM, once the classifier is trained, can perform the test automatically, speedily and on very large datasets.
The hierarchy of flow cytometry events produced by HCA is independent of person or laboratory where it is performed. The cluster selection can be formalized (description of dendrogram branches) and population size and structure can be discussed. This will allow a new level of easy and formally precise standardization and quality control in large international multicenter trials.
In summary, we have developed a new algorithm that allows applying HCA to flow cytometry and which opens new opportunities for the scientific and clinical use of flow cytometry. Most importantly, this approach reflects the multidimensionality and enables analysis of complex, multiparameter flow data. It provides a new tool to study tumor heterogeneity. From a clinical perspective, in combination with SVM learning, HCA using Mahalanobis-average linkage is applicable to leukemia diagnostics and MRD monitoring. It has been validated against a standardized set of flow MRD data from the I-BFM FLOW-MRD study group, and it has the potential for automatization.
K.F. designed research, wrote MATLAB scripts, analyzed data, prepared the figures and wrote the paper; T.S. developed Mahalanobis-average algorithm, wrote MATLAB scripts and critically reviewed the manuscript; A.S. retrieved data; B.W., J.I., E.M. and M.N.D. provided data and critically reviewed the manuscript.